Understanding and predicting traveler response to information: A literature review

Lappin, Jane; Bottom, Jon

doi:10.1037/e541572009-001

Cited by 23 publications

(13 citation statements)

References 102 publications

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“…It is also desirable that a response behavior model is capable of capturing the heterogeneity in drivers' taste (preferences) [4,19]. In the context of this study, the possible preference variations across individuals regarding travel time information and other alternative attributes will be appropriately addressed.…”

Section: Introductionmentioning

confidence: 99%

Leave the expressway or not? Impact of dynamic information

Gan

2014

J. Mod. Transport.

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This study investigates drivers' diversion decision behavior under expressway variable message signs that provide travel time of both an expressway route and a local street route. Both a conventional cross-sectional logit model and a mixed logit model are developed to model drivers' response to travel time information. It is based on the data collected from a stated preference survey in Shanghai, China. The mixed logit model captures the heterogeneity in the value of ''travel time'' and ''number of traffic lights'' and accounts for correlations among repeated choices of the same respondent. Results show that travel time saving and driving experience serve as positive factors, while the number of traffic lights on the arterial road, expressway use frequency, being a middle-aged driver, and being a driver of an employer-provided car serve as negative factors in diversion. The mixed logit model obviously outperforms the cross-sectional model in dealing with repeated choices and capturing heterogeneity regarding the goodness-of-fit criterion. The significance of standard deviations of random coefficients for travel time and number of traffic lights evidences the existence of heterogeneity in the driver population. The findings of this study have implications for future efforts in driver behavior modeling and advanced traveler information system assessment.

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Section: Introductionmentioning

confidence: 99%

Leave the expressway or not? Impact of dynamic information

Gan

2014

J. Mod. Transport.

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“…Molin and Timmermans (2006) evaluated the willingness to pay for additional information through web enabled public transport information systems, whereas, Grotenhuis et al (2007) investigated the desired quality of integrated multimodal travel information in public transport. Polydoropoulou and Ben-Akiva (1998), Lappin (2001), Chorus et al (2007) showed that the information acquisition can be explained by behavioural factors. In particular, Chorus et al (2007) discussed travellers' need for personalised and more advanced types of travel information.…”

Section: Introductionmentioning

confidence: 97%

The effect of Advanced Traveller Information Systems on public transport demand and its uncertainty

et al. 2011

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Advanced Traveller Information Systems (ATISs) include a broad range of advanced computer and communication technologies. These systems are designed to provide transit riders pre-trip and real-time information, to make better informed decisions regarding their mode of travel, planned routes and travel times. ATISs include in-vehicle displays, terminal or wayside based information centres, information by phone or mobile and internet. In this article, a Stated Preference survey has been carried out in order to know the preferences of public transport's customers related to different ATISs and their willingness to pay in Palermo. An ordered probit demand model has been calibrated to determine the potential additional share of demand attracted by the adoption of ATISs. Finally, Monte Carlo simulation has been carried out to appraise the uncertainty on some parameters of the calibrated demand model. The results show that respondents give more importance to the type of information and its cost, whereas they are less interested in the system that provides the information.

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“…But many problems also exist, such as whether receivers have the same understanding of or make same responses to the same traffic condition information provided by VMS. No one is yet able to accurately predict, for a VMS displaying a particular message at a particular location in a particular network, what the effect on individual travelers (Jane Lappin and Jon Bottom, December, 2001) [1] . For a particular message, how receivers who have dissimilar personal properties understand and react to.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent Based Traveler Route Choice Behavior Simulation Research when Supplied Individual Traffic Condition Information

Tang

Jiang

Yun

et al. 2009

2009 International Joint Conference on Artificial Intelligence

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When different drivers received the same traffic condition information through VMS (Variable Message Sigh), they may image the real traffic situation differently due to their dissimilar individual characteristics. Ignoring this dissimilarity and blindly supplying the same traffic condition information to all travelers cause VMS hard to supply high reliable information. This low reliability would induce travelers more likely to ignore what has been told by VMS. In this paper, a model was established to demonstrate the relationship between traffic-condition-division-speed and information-receivers' individual identities including gender, age, driving years, familiarity with road network and trip purpose. Then with the help of multi-agent simulator-SeSAm, a new model for supplying individual traffic condition information and simulating drivers' route choice behavior was established. It has been proved that providing individual information can significantly enhance the reliability of traffic condition information which leads to a dynamic flow balance more quickly than before.

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Understanding and predicting traveler response to information: A literature review

Cited by 23 publications

References 102 publications

Leave the expressway or not? Impact of dynamic information

Leave the expressway or not? Impact of dynamic information

The effect of Advanced Traveller Information Systems on public transport demand and its uncertainty

Multi-agent Based Traveler Route Choice Behavior Simulation Research when Supplied Individual Traffic Condition Information

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